{
 "metadata": {
  "name": "ch9"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n",
        "For more information, type 'help(pylab)'.\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "from pandas import *\n",
      "from sklearn.manifold import MDS\n",
      "from statsmodels.iolib.foreign import genfromdta\n",
      "import os\n",
      "from sklearn.metrics.pairwise import euclidean_distances\n",
      "import re\n",
      "import math"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Distance metrics of Senators by voting record"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The data live in the `data/roll_call` subfolder. These were converted to CSV using R's `read.dta` function in the `foreign` package.\n",
      "\n",
      "Converting them with statsmodels failed, presumably due to an incompatible Stata version (statsmodels can only read data from certain versions of Stata.)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "datdir = os.path.abspath(os.path.join('data', 'roll_call'))\n",
      "csv_files = [os.path.join(datdir, f) for f in os.listdir(datdir) \n",
      "                                     if f.endswith('.csv')]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We'll store the date for each Congress in a DataFrame, and collect all of them in a dictionary. We can then get the data for the 110th Congress, for example, by calling `roll_call[110]`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "roll_call = {}\n",
      "for f in csv_files:\n",
      "    s = re.search('sen(.*)kh', f)\n",
      "    congnum = int(f[(s.start()+3):(s.end()-2)])\n",
      "    roll_call[congnum] = read_csv(f)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Transform the voting codes to a simplified Yea (1), Nay (-1), No-vote (0) scheme."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def roll_call_simplified(df):\n",
      "    vote_codes = {1:  1,\n",
      "                  2:  1,\n",
      "                  3:  1,\n",
      "                  4: -1,\n",
      "                  5: -1,\n",
      "                  6: -1,\n",
      "                  7:  0,\n",
      "                  8:  0,\n",
      "                  9:  0,\n",
      "                  0:  0}\n",
      "    \n",
      "    # Remove the vice-president.\n",
      "    no_pres = df[df.state < 99]\n",
      "    \n",
      "    # Find the columns with vote data. These are typically 'V1', 'V915', etc.,\n",
      "    # but some dataset appear to have them as 'var1', 'var919', etc.\n",
      "    # Checking that the first letter is 'v' or 'V' will find these (and only)\n",
      "    # these columns.\n",
      "    vote_cols = [c for c in df.columns if c[0].upper() == 'V']\n",
      "\n",
      "    # Would like to just call replace on the whole df, but doesn't seem\n",
      "    # to work, so we'll apply replace to each column.\n",
      "    no_pres[vote_cols] = no_pres[vote_cols].apply(lambda x: x.replace(vote_codes))\n",
      "    return no_pres"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for cong in roll_call:\n",
      "    roll_call[cong] = roll_call_simplified(roll_call[cong])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We use scikit-learn's `euclidean_distances` function to compute the euclidean distances between observations (senators) in each Congress. The function takes a matrix of shape (No. of obs.) $\\times$ (No. of features/variables)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "distance = {}\n",
      "for cong in roll_call:\n",
      "    vote_cols = [c for c in roll_call[cong].columns if c[0].upper() == 'V' ]\n",
      "    distance[cong] = euclidean_distances(roll_call[cong][vote_cols].values)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Compute the MDS coordinates for the 110th Congress, and plot them."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rc110 = roll_call[110]\n",
      "dist110 = distance[110]\n",
      "dems = np.where(rc110.party == 100)[0]\n",
      "repubs = np.where(rc110.party == 200)[0]\n",
      "names = roll_call[110].name.values\n",
      "\n",
      "mds = MDS().fit_transform(dist110)\n",
      "\n",
      "plt.figure(figsize = (8, 5))\n",
      "# Plot invisible points to annotate with names\n",
      "plt.plot(mds[:, 0], mds[:, 1], '.', alpha = 0)\n",
      "plt.title('MDS analysis of Senators in the 110th Congress')\n",
      "# Plot dem names\n",
      "for i in dems:\n",
      "    plt.annotate(names[i], (mds[i, 0], mds[i, 1]),\n",
      "                 color = 'blue',\n",
      "                 alpha = 0.3, \n",
      "                 horizontalalignment = 'center', \n",
      "                 verticalalignment = 'center',\n",
      "                 family = 'sans-serif')\n",
      "# Plot repub names\n",
      "for i in repubs:\n",
      "    plt.annotate(names[i], (mds[i, 0], mds[i, 1]),\n",
      "                 color = 'red',\n",
      "                 alpha = 0.5, \n",
      "                 horizontalalignment = 'center', \n",
      "                 verticalalignment = 'center',\n",
      "                 family = 'sans-serif')\n",
      "\n",
      "    # Turn off axes labels.\n",
      "plt.setp(plt.gca().get_yaxis(), visible = False)\n",
      "plt.setp(plt.gca().get_xaxis(), visible = False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "[None]"
       ]
      },
      {
       "output_type": "display_data",
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gl8hIICUF2LUL6NmTAhXgdmPHAv8rzNEmbOMGhw6lsLCltBQwmjaDgoCkpLaf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FGnkQwNodUuWMB1Llzg/QvE/CvvSCneiZm6Y2ExJfDUUzqizeXCVCOBB62tlHxsb2ffDTdon2Nd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\nEEIcgqJKCCGEOARFlRBCCHGIfwDl6p1coQzTigAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And do the same for every Congress."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(nrows = 3, ncols = 4, figsize = (20, 10))\n",
      "plt.subplots_adjust(hspace = 0, wspace = 0)\n",
      "for a, cong in zip(ax.ravel(), roll_call):\n",
      "    rc = roll_call[cong]\n",
      "    dist = distance[cong]\n",
      "    dems = np.where(rc.party == 100)[0]\n",
      "    repubs = np.where(rc.party == 200)[0]\n",
      "    \n",
      "    mds = MDS().fit_transform(dist)\n",
      "\n",
      "    a.plot(mds[dems, 0], mds[dems, 1], '.b', mfc = 'white')\n",
      "    a.plot(mds[repubs, 0], mds[repubs, 1], 'xr')\n",
      "    # Label which Congress is being plotted\n",
      "    a.text(0, .99, cong, transform = a.transAxes,\n",
      "           verticalalignment = 'top', fontsize = 14)\n",
      "    # Turn off ticklabels\n",
      "    plt.setp(a.get_yaxis().get_ticklabels(), visible = False)\n",
      "    plt.setp(a.get_xaxis().get_ticklabels(), visible = False)\n",
      "\n",
      "# Turn off the 12th subplot\n",
      "plt.setp(ax[2, 3], visible = False)  "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "[None]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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2xiVQwmRyJfW1D1CBqfth3YrSUDraqagyKqjpPQtlBDmCRjwANxq1/5jN4VcE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\n4nsUp+nCoL6POxK+KluIS2jMVGVKmxjSZHTd5dbiAWkag9j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VAAAV\nRElEQVSPYLgKmtjclaEN8nZVSppEVj2PBMoQpLxxW9vCRxot8w+sVqD/3/r1Iv/7vwRpkoQ+roR+\n/ADgir8eNRkprtakpWsfPmxW0RSkaqO9XNV6ayhvyqKt9CnvfE1NmSDN7Gz9S0Rb6/sRibRxW0kp\nceFrS9rz8F1i1lac91pCH1dCOH5KnwD44GfXp6xslqo7MiVJS9cW8b/LBFrP1Y4HrE6Vk3e+bGYh\nscsFgpU2bmvYXruMrPmHltLstvFd2he40MeV0I8fAFxpPqMmb/cK27tbKFnpA5qifXVKW0aN9vMF\neJc1Lg8aCtfJhrCUUZF7bSkyv2DOoF4IzWfboE2/BzJqAPigc9cn2xOlJsqsABSiLVADIIfr0hRL\nCzSVdn1Keh7MGVTTXt7bFkV+D7EEcwjUAPCh+dKnIimuNhsT0jQYiuU1sgUA71yXpjTV5LfI82DO\noB7lvToU+T2woxMAVOenR00eWxMlm7XzNnvnAO9iEqMDATPAs6Z3jkoa00+eFNm6NZx+Oy3F7oU6\nFPk9VAmqMR4DgKEvUGMzuLKwYK9pME0G4QArgzoQMAM8azqTJWlM375d5MABNhpQjuazOqT9HoYD\nLffcUz6oxngMAIa+QI3N4IrNdO2mUrPRKqwM6kDADPDIx85RSWP600+LrF+/9uvYfQgobDjQsn9/\n+aAa4zEAGM03E9aianNEmgwCtWhsJhzSzk+xNGdEw1w3BK6jqV2fkjCmr6X5tQL1pqZMkGZ2ttoi\nlMbxOPp7IgAq6cuoaUqVUiaaDAJRaiqV3kbtPWnhqMRH+W7R3m6umxWnYUxPRqk3aqibKUxpGwAY\nOgI1Phr1li1l8pGaDVRAI75mVDnPNoIspIWjEh/lu5pv+BnT01HqjRrqBlqYwwCAoSNQ42syV2aX\nCZu9cwCHyLior8hEscp5thFkoa8RKmt6ZyXNN/xVx/S27ADZ9GsFeBdzGAAwdARqbE7mykyiyqQ9\nj6ZmDx5vODU7xskagkPGRX1FJopVzrONIAtp4ajMR6mP1hv+quVWmrOEbKIsDJaUzZBhDgMAho5A\njYi9yVzRSVTdtOe2TNYQHDIu6isyUaxyntsUZCF9XRlfpT6x3fBrzhKyhbIwWJS18JE0TjCHAQBD\nz65Pg4nB5KSZzNW5Qhd5LBu7Gtg8ZiBFbLv8aNv1Ken8atx1IjTT0+Zft2sm3XNzvo+o5Xzs5DN8\nwz/4w3IY2Gj02hLzblHs+tQ6LucZWbtAhTJOsOsTAB90ZNTYXr0pkp1jY5cJrSndiAr12m4lnd82\nZb64Qvq6Mj52Voq1t1tsWUKjfO3CBW9szDPSsiizMmQYJwAgnY5Aje3JXFOTqNgna1CBiYwbg0nl\nr37l//zGWCZE+npg6jbJTfr+iYm143joN/yUBSFCNuYZacGerIUPxgkASKcjUGNTU5MoJmtoCBMZ\nNwaTygcfFNm2ze/5jTFriqykwNTtu9aWvm2xZgmh1fLmGUUWE6oEexgnACCdjkCNzQleU5MoJmto\nCBMZNwaTyv/6L5Hvf3/t+c2bmNrMgiFrCt7VbZLbhia79G5BpPLmGUUWE1hUAgC74mwmXBWTMMA5\nLc2E8xoG5zU5tNkEkebFUKNuk1yPTXadX1sabo4MaJHVENgVTRsp0EwYgA86MmpEdDTm1Za6Xbdn\nAKIWY1+TJuWtIOZludjMgiFrCirU7bsWe9+2NmQNAQlcZMvkzWFiLAkGgDL0BGo0TPBcTMLqBFu0\nBY6gCpMYt/ImpqR5Iyp1+661pW+bhkUloGGjiwk2Fory5jCUBANoOx2BGk0TPNuTsDrBFlbvkIFJ\njFt5WS5kwSAqdfuuVf3+0DJHNSwqAZ7ZWCjKm8OwGAKg7XQEalw35i0zEbQ9CbPRoJHVOyRgEgPA\nmptvXnshKbOVdtXvDyFzdDCHGO1Js2lTnFlDQA4bC0W7d4vce6/I3/6tyH/8x9o/IxZDALSdnmbC\nRVRt9lu0AaDLRoFVGyxqaLIMWKSlmTAAJSyNc86uLYPj27RJZPNm87HB3ECEDQdQmqZGuVUMN8B/\n4ons55L1XG025XdJ5T0RgOjpyKgpqurKW1pWy8LC6hD+woIpuB1k8tjK7KmapaOpJAxApiabO9NI\nuiGhleXU5ev5as8cHcwFjh5du4BTJusIeJe2HnNlx5ThbJe855L1eUq4ASBdWIGaOmVESRPB0cDP\nxITIvn2rAz91J2F1gi2uS8IArFE1CNLkxFvbJD9aIZTl2OTr+YbQ96VKMKltgT4Upi1AUWdMKbND\n4mWXrR5fKeEGgAz9FKdOneqfOnUq7dN+dbv9voh5W9SZM/3+9u3me7ZvN+9nfdyW555b+5hnzpiP\nAy306quv6r229Pv9Bx4wb//wh35/587i3zc5ab4n6TLyrW+Zx52ctHOJyfpZsMz1GKFN08938PNG\nx+QKP9fptaXKebH43BCXM2fM+KLlpVBnTMl7LsOfrzq++qb6nghAtMIL1CRNlvKCIXmTpSqBn6Jc\nBWoIACFQ2gM1VSesWZNV25NTbZP86LkcIzRq8vlaHMucXVvqBFzaFuhDkOqOKUUXI0JdZFB7TwQg\namGVPqWVEW3cmJ2unVVC5Drl2lUqedtS8oGGVE3FztqhwnaaO7thNCiEshybmn6+dXebakKdMmTt\n/XcAqT+mFC2dGh5fn3iCXmsAkCktgqMyepy18qY5LdnVihordQiQ9owaF8iACVTbSleafL4OskJV\nXlsYpxGh0QyaKpkyIZVBqbwnAhC9sLbnzlN2C+yq231X8dRTInfeufrYbPysqtt+A55o3J479K1S\n4UiTY4QGTT7f0d2TRt+vQN21ZfQ5HTwo8pOfmNS6wXOM+fWEYJQdA0e31d6zZ2W77qJ/vlNTJgNn\ndlZ/M+Eg74kABC+s0qesHRSqpGs3lXLd64n8/OciX/2qyL//u3nfRqlS21LyAUds7KLEltkR0lqW\n42o3oSafb51dHEMxWjK1ebN5e/iweUvJMpQoOwaOlvNWKZ1ixycAyBZWoCatL8ugR02VLbDT2JoI\nD45xdlZk/37zsX/5F5Fdu+qNTnW2/Qawio0eMmyZjcbE0qMs9v4to4GvTsdcZI4ejTc4pRSB9GxF\nxsDhc3jPPdWCLMOPIUKvNQDIElagJm0F7v/+r3qjvzS2JsLDK2qdjsj994v8z/+IXH99vdGpTnND\nAKvYWNmz3TAYSBVCNkqRxY42ZoXGHpxSikB6tiJj4PA53L9/JchSJgjG7wEAigsrUCOSPMlxka5t\nayI8fGzDk9Jjx+pNSrWm5AMBGk3brrL6Sho3GqX9hj9vsaOtWaFtDE4p0MZAeplxrEjpUto5TAu+\nJP38Nv4eAKCq8AI1TU5yqk6Ek1YST54U2bq1fZNSIEBVVv00bplNun/EtN/w5y12tDErtK3BKQXa\nGEi3nb2Sdg7LBHDa+HsAgKrCCtQ0PcmpOhFOWkncvl3kwIF2TUqBQMWy6keaeaRCueHPWuxoY1Zo\nG4NTSmgMpLtmexwbnMMnnli9AFAmgNPG3wMAVBVWoKbMJKdsM+DRr+/1TMPfTZvKT4STVhKfflpk\n/fq1X5c0KbXRyNjVriBAC5Rd9dOauRJLwAkjQrnh157107Q2BqfgjavsldEFgLTgC9kzAFBPWIGa\nMpOcss2AR79+sH3mYDvNshPhOv0DbDQyjmVXkFARKAta2VU/rZkrTJQjFcINfyhZP0Ck6mavpC1A\nFF0AIHsGAOoJK1BTRtlmwKNff/SoGYVGt9YsOhGus5Joo5Gx7V1BCDyUQ6CsVbRmrjBRrojrXX2h\nZP0ALVQkCzRtAWJ4AWC0DAoAYE+8gRqR8lktRb6+6JajaSuJRW8AbOzoYXNXEM2BB403VSFsnwtr\nyFyJjObrXShCyPoBWqpIFmjaAsTwAoDWbFIAiEHcgZqyWS1Fvr7IBD5rJbHoDYCN2n6b/QE0Bx60\n3lRp3z4X1jSZuaK1H05UNF/vsmgMWgNQp0gWaJEFCK3ZpAAQhX6KU6dO9U+dOpX2af3OnOn3t283\nb5Per/P1g891u9mPmXdsad9f9tjrPp8yut1+X8S81aTu78QFjcekwKuvvhrEteVb3+r3H3ig35+c\n1PWre+AB8/YPf+j3d+70eijx03q9S+Pquh+IUK4tgG9nzpjxI+/SkDcOFn2c0AV/TwQgSHEFap57\nbmW0GPz/zBnz/35/9f+zvncg6+vrTuCzvr/ssSSx8RijtAceNN1UtfyGKUsoN1NaAyKTk+aYeDk5\npv16l0bDcbsYfwoI5doCFKFhsUDrONi0IO+JAAQvrtKn4RKYQR38cAlMVn18mXr6uiVFed9vo7bf\ndn8A7Tt4aNsGlkaawdOa0k0/nAZov95l0VByqbUcFQiIhv4vWsdBAGiDdf1+v5/0iaWlJRERGR8f\nb/SAahtMCCcnzQ277buZ4Ql8p7P2fdff78v8vJlkDx9jr2cCD76bQ1o6pzMzZmK0vGxuhjX/OkK2\nuLgoY2Nj6q8tvZ6ZHO/Zk/5a4DUTKc3Xuzyux0DFxxHKtaWykF+XKG1qygRpZmfT/3xcj0FFxsE2\nCPaeCEDQ4gvUiJjmjxs2mGWAq6+2+9h1J0pFv58JWXGWztX0tPnX7ZqMhbk560cKietmitcMVNG2\nEOByLE4Q07UlkbbfL5wqEiRhDGpG0PdEAIIVV+mTiJ0SmKydM+qWFBX9flLHi7NU5kWKL8rKes2E\nsDtTCMeIEjSVXGorR41Bld3I2AksWEV2E2TeAgDxiitQY6uvgK0gSZ0JUtkJGZOx2uj9gbKyXjMa\n+gvkCeEYUYLt3mRVhdzjR7uyPYhY9AlaXjDd5ryFwD0A6BJXoMbWamKVVaskdSdIZSZkTMZqK7J6\nB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      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}